diff --git a/src/main/java/org/apache/commons/math/optimization/fitting/GaussianFunction.java b/src/main/java/org/apache/commons/math/optimization/fitting/GaussianFunction.java index 7a3ce4a81..6100f537d 100644 --- a/src/main/java/org/apache/commons/math/optimization/fitting/GaussianFunction.java +++ b/src/main/java/org/apache/commons/math/optimization/fitting/GaussianFunction.java @@ -43,7 +43,6 @@ import org.apache.commons.math.exception.NullArgumentException; * * * @see GaussianDerivativeFunction - * @see ParametricGaussianFunction * @since 2.2 * @version $Revision$ $Date$ */ diff --git a/src/main/java/org/apache/commons/math/optimization/fitting/ParametricGaussianFunction.java b/src/main/java/org/apache/commons/math/optimization/fitting/ParametricGaussianFunction.java deleted file mode 100644 index 0b430359a..000000000 --- a/src/main/java/org/apache/commons/math/optimization/fitting/ParametricGaussianFunction.java +++ /dev/null @@ -1,143 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.commons.math.optimization.fitting; - -import java.io.Serializable; - -import org.apache.commons.math.exception.DimensionMismatchException; -import org.apache.commons.math.exception.util.LocalizedFormats; -import org.apache.commons.math.exception.ZeroException; -import org.apache.commons.math.exception.NullArgumentException; -import org.apache.commons.math.analysis.ParametricUnivariateRealFunction; - -/** - * A Gaussian function. Specifically: - *

- * {@code f(x) = a + b*exp(-((x - c)^2 / (2*d^2)))} - *

- * The parameters have the following meaning: - *

- * Notation key: - * - * References: - * - * - * @since 2.2 - * @version $Revision$ $Date$ - */ -public class ParametricGaussianFunction implements ParametricUnivariateRealFunction, Serializable { - /** Serializable version Id. */ - private static final long serialVersionUID = -3875578602503903233L; - - /** - * Computes value of function {@code f(x)} for the specified {@code x} and - * parameters {@code a}, {@code b}, {@code c}, and {@code d}. - * - * @param x Value at which to compute the function. - * @return {@code f(x)}. - * @param parameters Values of {@code a}, {@code b}, {@code c}, and {@code d}. - * @throws NullArgumentException if {@code parameters} is {@code null}. - * @throws DimensionMismatchException if the size of {@code parameters} is - * not 4. - * @throws ZeroException if {@code parameters[3]} is 0. - */ - public double value(double x, double[] parameters) { - validateParameters(parameters); - final double a = parameters[0]; - final double b = parameters[1]; - final double c = parameters[2]; - final double d = parameters[3]; - final double xMc = x - c; - return a + b * Math.exp(-xMc * xMc / (2.0 * (d * d))); - } - - /** - * Computes the gradient vector for a four variable version of the function - * where the parameters, {@code a}, {@code b}, {@code c}, and {@code d}, - * are considered the variables, not {@code x}. That is, instead of - * computing the gradient vector for the function {@code f(x)} (which would - * just be the derivative of {@code f(x)} with respect to {@code x} since - * it's a one-dimensional function), computes the gradient vector for the - * function {@code f(a, b, c, d) = a + b*exp(-((x - c)^2 / (2*d^2)))} - * treating the specified {@code x} as a constant. - *

- * The components of the computed gradient vector are the partial - * derivatives of {@code f(a, b, c, d)} with respect to each variable. - * That is, the partial derivative of {@code f(a, b, c, d)} with respect to - * {@code a}, the partial derivative of {@code f(a, b, c, d)} with respect - * to {@code b}, the partial derivative of {@code f(a, b, c, d)} with - * respect to {@code c}, and the partial derivative of {@code f(a, b, c, - * d)} with respect to {@code d}. - * - * @param x Value to be used as constant in {@code f(x, a, b, c, d)}. - * @param parameters Values of {@code a}, {@code b}, {@code c}, and {@code d}. - * @return the gradient vector of {@code f(a, b, c, d)}. - * @throws NullArgumentException if {@code parameters} is {@code null}. - * @throws DimensionMismatchException if the size of {@code parameters} is - * not 4. - * @throws ZeroException if {@code parameters[3]} is 0. - */ - public double[] gradient(double x, double[] parameters) { - validateParameters(parameters); - final double b = parameters[1]; - final double c = parameters[2]; - final double d = parameters[3]; - - final double xMc = x - c; - final double d2 = d * d; - final double exp = Math.exp(-xMc * xMc / (2 * d2)); - final double f = b * exp * xMc / d2; - - return new double[] { 1.0, exp, f, f * xMc / d }; - } - - /** - * Validates parameters to ensure they are appropriate for the evaluation of - * the {@code value} and {@code gradient} methods. - * - * @param parameters Values of {@code a}, {@code b}, {@code c}, and {@code d}. - * @throws NullArgumentException if {@code parameters} is {@code null}. - * @throws DimensionMismatchException if the size of {@code parameters} is - * not 4. - * @throws ZeroException if {@code parameters[3]} is 0. - */ - private void validateParameters(double[] parameters) { - if (parameters == null) { - throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY); - } - if (parameters.length != 4) { - throw new DimensionMismatchException(4, parameters.length); - } - if (parameters[3] == 0) { - throw new ZeroException(); - } - } -} diff --git a/src/site/xdoc/changes.xml b/src/site/xdoc/changes.xml index c455f5fca..ed96cbf99 100644 --- a/src/site/xdoc/changes.xml +++ b/src/site/xdoc/changes.xml @@ -52,6 +52,10 @@ The type attribute can be add,update,fix,remove. If the output is not quite correct, check for invisible trailing spaces! --> + + Removed "ParametricGaussianFunction" (in package "optimization.fitting"); + functionality moved to class "Gaussian" (in package "analysis.function"). + Refactored "GaussianFitter" (in package "optimization.fitting"). The class now really fits a Gaussian function (whereas previously it was diff --git a/src/test/java/org/apache/commons/math/optimization/fitting/ParametricGaussianFunctionTest.java b/src/test/java/org/apache/commons/math/optimization/fitting/ParametricGaussianFunctionTest.java deleted file mode 100644 index baa286962..000000000 --- a/src/test/java/org/apache/commons/math/optimization/fitting/ParametricGaussianFunctionTest.java +++ /dev/null @@ -1,158 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.commons.math.optimization.fitting; - -import org.apache.commons.math.exception.MathUserException; -import org.apache.commons.math.exception.ZeroException; -import org.apache.commons.math.exception.MathIllegalArgumentException; -import org.apache.commons.math.optimization.OptimizationException; -import org.apache.commons.math.optimization.fitting.CurveFitter; -import org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer; -import org.junit.Test; - -import static org.junit.Assert.assertEquals; - -/** - * Tests {@link ParametricGaussianFunction}. - * - * @since 2.2 - * @version $Revision$ $Date$ - */ -public class ParametricGaussianFunctionTest { - /** Dataset 1 used by some test cases. */ - protected static final double[][] DATASET1 = new double[][] { - {4.0254623, 531026.0}, - {4.02804905, 664002.0}, - {4.02934242, 787079.0}, - {4.03128248, 984167.0}, - {4.03386923, 1294546.0}, - {4.03580929, 1560230.0}, - {4.03839603, 1887233.0}, - {4.0396894, 2113240.0}, - {4.04162946, 2375211.0}, - {4.04421621, 2687152.0}, - {4.04550958, 2862644.0}, - {4.04744964, 3078898.0}, - {4.05003639, 3327238.0}, - {4.05132976, 3461228.0}, - {4.05326982, 3580526.0}, - {4.05585657, 3576946.0}, - {4.05779662, 3439750.0}, - {4.06038337, 3220296.0}, - {4.06167674, 3070073.0}, - {4.0636168, 2877648.0}, - {4.06620355, 2595848.0}, - {4.06749692, 2390157.0}, - {4.06943698, 2175960.0}, - {4.07202373, 1895104.0}, - {4.0733171, 1687576.0}, - {4.07525716, 1447024.0}, - {4.0778439, 1130879.0}, - {4.07978396, 904900.0}, - {4.08237071, 717104.0}, - {4.08366408, 620014.0} - }; - - /** - * Using not-so-good initial parameters. - * - * @throws OptimizationException in the event of a test case error - * @throws MathUserException in the event of a test case error - */ - @Test - public void testFit01() - throws OptimizationException, MathUserException { - CurveFitter fitter = new CurveFitter(new LevenbergMarquardtOptimizer()); - addDatasetToCurveFitter(DATASET1, fitter); - double[] parameters = fitter.fit(new ParametricGaussianFunction(), - new double[] {8.64753e3, 3.483323e6, 4.06322, 1.946857e-2}); - assertEquals(99200.94715858076, parameters[0], 1e-4); - assertEquals(3410515.221897707, parameters[1], 1e-4); - assertEquals(4.054928275257894, parameters[2], 1e-4); - assertEquals(0.014609868499860, parameters[3], 1e-4); - } - - /** - * Using eye-balled guesses for initial parameters. - * - * @throws OptimizationException in the event of a test case error - * @throws MathUserException in the event of a test case error - */ - @Test - public void testFit02() - throws OptimizationException, MathUserException { - CurveFitter fitter = new CurveFitter(new LevenbergMarquardtOptimizer()); - addDatasetToCurveFitter(DATASET1, fitter); - double[] parameters = fitter.fit(new ParametricGaussianFunction(), - new double[] {500000.0, 3500000.0, 4.055, 0.025479654}); - assertEquals(99200.81836264656, parameters[0], 1e-4); - assertEquals(3410515.327151986, parameters[1], 1e-4); - assertEquals(4.054928275377392, parameters[2], 1e-4); - assertEquals(0.014609869119806, parameters[3], 1e-4); - } - - /** - * The parameters array is null. - * - * @throws MathUserException in the event of a test case error - */ - @Test(expected=MathIllegalArgumentException.class) - public void testValue01() throws MathUserException { - ParametricGaussianFunction f = new ParametricGaussianFunction(); - f.value(0.0, null); - } - - /** - * The parameters array length is not 4. - * - * @throws MathUserException in the event of a test case error - */ - @Test(expected=MathIllegalArgumentException.class) - public void testValue02() throws MathUserException { - ParametricGaussianFunction f = new ParametricGaussianFunction(); - f.value(0.0, new double[] {0.0, 1.0}); - } - - /** - * The parameters d is 0. - * - * @throws MathUserException in the event of a test case error - */ - @Test(expected=ZeroException.class) - public void testValue03() throws MathUserException { - ParametricGaussianFunction f = new ParametricGaussianFunction(); - f.value(0.0, new double[] {0.0, 1.0, 1.0, 0.0}); - } - - /** - * Adds the specified points to specified CurveFitter instance. - * - * @param points data points where first dimension is a point index and - * second dimension is an array of length two representing the point - * with the first value corresponding to X and the second value - * corresponding to Y - * @param fitter fitter to which the points in points should be - * added as observed points - */ - protected static void addDatasetToCurveFitter(double[][] points, - CurveFitter fitter) { - for (int i = 0; i < points.length; i++) { - fitter.addObservedPoint(points[i][0], points[i][1]); - } - } -}